Control Models Research Articles

Morphological changes to peripheral joints supporting thumb trapezial metacarpal osteoarthritis.

The present study aims to determine if morphological differences of the scaphoid, trapezoid, and second metacarpal are associated with thumb osteoarthritis by comparing three-dimensional bone models of healthy controls and osteoarthritis patients. Fifty-eight patients with moderate to severe thumb osteoarthritis (modified Eaton≥II) and 35 healthy controls from a larger completed investigation were examined. To quantify morphological variations, a statistical shape model was created that classified binary with respect to the Eaton grading system: non-osteoarthritis or moderate/severe osteoarthritis (II-IV). Three-dimensional surface models based on computed tomography images from the scaphoid, trapezoid, and second metacarpal were used to "train" the statistical shape model and yielded features that best explain variation within the three bones: the principal components These principal components were tested for significant differences between patient and control group. Additionally, a statistical shape model entailing all three bones was created. For the second metacarpal, only a single principal component was significantly associated with osteoarthritis (p=0.035). The combined model utilizing all three bones, however, showed that with using one principal component of each of the bones, we could classify a sample as moderate/severe trapeziometacarpal osteoarthritis with an accuracy of 0.77. No individual shape components of the scaphoid or trapezoid significantly correlated to osteoarthritis. This study affirms that basilar thumb osteoarthritis is not limited to the trapeziometacarpal joint. Future studies investigating surrounding bones and joints as contributors to disease occurrence or progression will provide a more holistic insight into the prevention, diagnostic, and treatment of thumb osteoarthritis.

Is maternal diabetes during pregnancy associated with neurodevelopmental, cognitive and behavioural outcomes in children? Insights from individual participant data meta-analysis in ten birth cohorts

BackgroundGrowing evidence shows that dysregulated metabolic intrauterine environments can affect offspring’s neurodevelopment and behaviour. However, the results of individual cohort studies have been inconsistent. We aimed to investigate the association between maternal diabetes before pregnancy and gestational diabetes mellitus (GDM) with neurodevelopmental, cognitive and behavioural outcomes in children.MethodsHarmonised data from > 200 000 mother-child pairs across ten birth cohorts in Europe and Australia were available. Mother-child pairs were included for analysis to determine whether GDM was recorded (yes or no) and whether at least one neurodevelopmental, cognitive and behavioural outcome was available in children aged 3 to 13 years. Confounder-adjusted regression models were used to estimate associations between maternal diabetes and child outcomes using two-stage individual participant data (IPD) meta-analysis. Model 1 included a crude estimate. The full adjustment model (model 2) included adjustment for child sex, maternal age, pre-pregnancy BMI, pregnancy weight gain, maternal smoking during pregnancy, plurality, parity and maternal education.ResultsChildren (aged 7–10 years) born to mothers with GDM had higher attention-deficient hyperactive disorder (ADHD) symptoms compared to non-exposed controls (model 2, regression coefficient (β) 3.67 (95% CI 1.13, 6.20), P = 0.001). Moreover, children (aged 4–6 years) born to mothers with GDM exhibited more externalising problems than those born to mothers without GDM (model 2, β 2.77 (95% CI 0.52, 5.02), P = 0.01). A pre-existing maternal history of type 1 and type 2 diabetes mellitus was associated with ADHD symptoms at 4–6 years (model 1, β 8.82 (95% CI 2.21, 15.45, P = 0.009) and β 7.90 (95% CI 0.82, 14.98, P = 0.02), respectively). The association was no longer apparent in further adjustments.ConclusionsThis study found that children between 4 - 6 and 7–10 years of age born to mothers with GDM have a greater likelihood of developing externalising problems and ADHD symptoms, respectively. Externalising problems often co-exist with ADHD symptoms and precede formal ADHD diagnosis. Overall, this large-scale multi-cohort study suggested that a dysregulated metabolic environment during pregnancy may contribute to ADHD symptoms and externalising problems in young children.

Thermal characterization, modeling and parametric model order reduction (PMOR) of piezoelectric transducers

Abstract The consideration of the thermals of piezoelectric transducers has gained relevance due to the increasing demands on the performance of actuators as well as the special applications such as high-power, high-precision or high-temperature applications. In the state of the art, many works deal with the representation of the temperature-dependence of piezoelectric performance and the development of suitable systems that are optimized in terms of self-heating. The prerequisite for this, is to characterize the actuators thermally and to have thermal models of piezoelectric actuators, that represent the temperature of the actuator. In this paper a workflow is presented for the thermal characterization and modeling of piezoelectric transducers in a fast and non-destructive manner. Aim of this work is to generate Finite-Element (FE) Models and Reduced Order Models (ROM) of the thermal behaviour of piezoelectric transducers for many different sizes of actuators. For this purpose an experimental setup and characterization workflow are developed. The advantage of the workflow lies in the speed of characterization through direct comparison of measurement results with a Metamodel and automated model order reduction. Parametric model order reduction (PMOR) is also investigated in this context for faster model building and compared to the results of FE and ROM. Model order reduction offers the advantage that the resulting models are low in computing effort, can be used without FE software and can directly be included in for example control or system models. Finally by direct comparison with a simple circuit model as in the state of the art the advantages of FE modeling can be shown. The workflow can be used for many different test specimen that vary in size and shape. The method is exemplarily presented using a Lead Zirconate Titanate (PZT) multilayer actuator.

Addressing racial and ethnic disparities in premature exits from permanent supportive housing among residents with substance use disorders

BackgroundPermanent supportive housing (PSH) is an evidence-based practice for reducing homelessness that subsidizes permanent, independent housing and provides case management—including linkages to health services. Substance use disorders (SUDs) are common contributing factors towards premature, unwanted (“negative”) PSH exits; little is known about racial/ethnic differences in negative PSH exits among residents with SUDs. Within the nation’s largest PSH program at the Department of Veterans Affairs (VA), we examined relationships among SUDs and negative PSH exits (for up to five years post-PSH move-in) across racial/ethnic subgroups.MethodsWe used VA administrative data to identify a cohort of homeless-experienced Veterans (HEVs) (n = 2,712) who were housed through VA Greater Los Angeles’ PSH program from 2016–2019. We analyzed negative PSH exits by HEVs with and without SUDs across racial/ethnic subgroups (i.e., African American/Black, Non-Hispanic White, Hispanic/Latino, and Other/Mixed [Asian, American Indian or Alaskan Native, and Native Hawaiian or Other Pacific Islander, and mixed race/ethnicity]) in controlled models and accounting for competing risk of death.ResultsIn competing risk models, HEVs with at least one SUD had 1.3 times the hazard of negative PSH exits compared to those without SUDs (95% CI: 1.00, 1.61). When stratifying by race/ethnicity, Other/Mixed race residents with at least one SUD had 6.4 times the hazard of negative PSH exits compared to their peers without SUDs (95% CI: 1.61–25.50). Hispanic/Latino residents with at least one SUD had 1.9 times the hazard compared to those without SUDs; however, this association was not statistically significant (95% CI: 0.85–4.37). African American/Black residents with at least one SUD had 1.2 times the hazard compared to those without SUDs (95% CI: 0.85–1.64), indicating no evidence of an association with negative PSH exits. Non-Hispanic White residents with at least one SUD had 1.1 times the hazard compared to those without SUDs (95% CI: 0.75–1.66), similarly indicating no evidence to suggest an association with negative PSH exits.ConclusionsThese findings suggest relationships between SUDs and negative PSH exits differ between racial/ethnic groups and suggest there may be value in culturally specific tailoring and implementation of SUD services for these subgroups.

Multi-scale channel attention U-Net: a novel framework for automated gallbladder segmentation in medical imaging

ObjectivesTo develop a novel automatic delineation model, the Multi-Scale Channel Attention U-Net (MCAU-Net) model, for gallbladder segmentation on CT images of patients with liver cancer.MethodsWe retrospectively collected the CT images from 120 patients with liver cancer, based on which ground truth was manually delineated by physicians. The images and ground truth constitute a dataset, which was proportionally divided into a training set (54%), a validation set (6%), and a test set (40%). Data augmentation was performed on the training set. Our proposed MCAU-Net model was employed for gallbladder segmentation and its performance was evaluated using Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), Positive Predictive Value (PPV), Sensitivity (SE), Hausdorff Distance (HD), Relative Volume Difference (RVD), and Volumetric Overlap Error (VOE) metrics.ResultsOn the test set, MCAU-Net achieved DSC, JSC, PPV, SE, HD, RVD, and VOE values of 0.85 ± 0.22, 0.79 ± 0.23, 0.92 ± 0.14, 0.84 ± 0.23, 2.75 ± 0.98, 0.18 ± 0.48, and 0.22 ± 0.42, respectively. Compared to the control models, U-Net, SEU-Net and TransUNet, the MCAU-Net improved DSC 0.06, 0.04 and 0.06, JSC by 0.09, 0.06 and 0.09, PPV by 0.08, 0.08 and 0.05, SE by 0.05,0.05 and 0.07, and reduced HD by 0.45, 0.28 and 0.41, RVD by 0.07, 0.03 and 0.07, VOE by 0.04, 0.02 and 0.08 respectively. Qualitative results revealed that MCAU-Net produced smoother and more accurate boundaries, closer to the expert delineation, with less over-segmentation and under-segmentation and improved robustness.ConclusionsThe MCAU-Net model significantly improves gallbladder segmentation on CT images. It satisfies clinical requirements and enhances the efficiency of physicians, particularly in segmenting complex anatomical structures.

Enhancing power system stability using a TCSC-PIDF controller with innovative multiobjective function and hybrid optimisation logic

This study aims to establish a thyristor-controlled series compensator (TCSC) equipped with a proportional integral derivative with filter (PIDF) controller by using a futuristic optimisation technique called evolutionary programming sine cosine algorithm (EPSCA) with multiobjective function (MOF). EPSCA is developed by merging evolutionary programming and the sine cosine algorithm. Three stability indicators, i.e. damping ratio (DR), damping factor (DF) and the greatest imaginary component of the system eigenvalues at a certain ratio, are combined to form the MOF. EPSCA is designed to optimise TCSC-PIDF controller variables to reap a peerless solution with a futuristic MOF as an objective function (OF). All simulations are performed using a linearised dynamical system of the TCSC-single machine infinite bus system. In addition, the TCSC-PIDF controller using EPSCA based on MOF is compared with other indicators, such as DR and DF, as the OF. The MOF-EPSCA-derived PIDF damping controller manifests substantial ameliorations in settling times and overshoot in comparison with DR-EPSCA, DF-EPSCA, and PIDF-U. The suggested method of optimisation, which combines a futuristic multiobjective function and damping controller model, validates the robustness of the damping controller’s design for the power system.

A Comprehensive Survey on the Requirements, Applications, and Future Challenges for Access Control Models in IoT: The State of the Art

The Internet of Things (IoT) is a technologyof connecting billions of devices with heterogeneous types and capabilities. Even though it is an attractive environment that could change the way we interact with the devices, the real-life and large-scale implementation of it is greatly impeded by the potential security risks that it is susceptible to. While the potential of IoT is significant, the security challenges it faces are equally formidable. IoT security can be addressed from different angles, but one of the key issues is the access control model because among the many challenges, access control is a pivotal concern that determines the overall security of IoT systems. This eventually determines which device is given access to the IoT systems and which is denied access. In this work, we conduct a systematic and thorough survey on the state-of-the-art access control models in IoT. This study includes more than 100 related articles, including 77 best-quartile journal papers. We cover conventional as well as advanced access control models, taking the crucial period of various studies in this particular area. In addition, a number of critical questions are answered and key works are summarized. Furthermore, we identify significant gaps in existing models and propose new considerations and prospects for future developments. Since no existing survey explores both conventional and sophisticated access control models with essential challenges, trends and application domains analysis, and requirements analysis, our study significantly contributes to the literature, especially in the IoT security field.

Plug-and-play myoelectric control via a self-calibrating random forest common model.

Electromyographic (EMG) signals show large variabilities over time due to factors such as electrode shifting, user behaviour variations, etc., substantially degrading the performance of myoelectric control models in long-term use. Previously one-time model calibration was usually required each time before usage. However, the EMG characteristics could change even within a short period of time. Our objective is to develop a self-calibrating model, with an automatic and unsupervised self-calibration mechanism. We developed a computationally efficient random forest (RF) common model, which can (1) be pre-trained and easily adapt to a new user via one-shot calibration, and (2) keep calibrating itself once in a while by boosting the RF with new decision trees trained on pseudo-labels of testing samples in a data buffer. Our model has been validated in both offline and real-time, both open and closed-loop, both intra-day and long-term (up to 5 weeks) experiments. We tested this approach with data from 66 able-bodied participants. We also explored the effects of bidirectional user-model co-adaption in closed-loop experiments. We found that the self-calibrating model could gradually improve its performance in long-term use. With visual feedback, users will also adapt to the dynamic model meanwhile learn to perform hand gestures with significantly lower EMG amplitudes (less muscle effort). Our random forest-based approach provides a new alternative built on simple decision tree for myoelectric control applications, which is explainable, computationally efficient, and requires minimal data for model calibration.

Developing grid-forming converter controller DLL for real-time HIL simulations

Abstract There is a lack of knowledge on the development of a controller model into a black box dynamically-linked library (DLL) file which can be flexibly tested with a power system model through a real-time co-simulation platform. Developing a test bed to demonstrate the performance and compliance analysis enhances the approval of novel control and protection strategies. This paper presents the development of a controller DLL and a test bench that integrates this DLL with a real-time simulator. The DLL contains a previously developed grid-forming controller for the type‑3 wind turbine and works in co-simulation with the power system model on Real-Time Digital Simulator (RTDS) in real-time.

Finite element analysis of stress in mandibular molars repaired after fractured instrument removal

BackgroundThis study assessed stress distributions in simulated mandibular molars filled with various materials after the removal of fractured instruments from the apical thirds of the root canals.MethodsFinite element models of the mesial and distal root canals were created, where fractured instruments were assumed to be removed using a staging platform established with a modified Gates-Glidden bur (Woodpecker, Guangxi, P.R. China). Each canal was treated with different materials: Biodentine (Septodont, Saint Maur-des-Fosses, France), mineral trioxide aggregate (MTA), and gutta-percha. In addition to these, four control models were also created. In total, 14 models were generated and subjected to a 300 N force applied at a 45° angle to simulate the stress.ResultsModel 1 (Biodentine) and 2 (Gutta-percha + Biodentine) showed the lowest stresses among mesial root canal models, whereas Model 5 (Gutta-percha) showed the highest. Among distal root canal models, Model 6 (Biodentine) and 7 (Gutta-percha + Biodentine) showed the lowest stresses, while Model 10 (Gutta-percha) showed the highest. In addition, Biodentine (Septodont) and Gutta-percha + Biodentine (Septodont) produced similar stress levels in both mesial and distal roots, while MTA and Gutta-percha + MTA led to increased stresses, especially in the distal root. Among the control models, the highest maximum von Mises stress values were in Model 13 and Model 14, which had a staging platform and were not filled with any root canal filling material, after the broken instrument was removed.ConclusionsBiodentine (Septodont) may be preferable to MTA for filling staging platform cavities post-fractured instrument removal due to lower stress levels. Furthermore, calcium silicate-based materials alone or in combination with Gutta-percha showed similar stress levels, suggesting their potential use for root canal filling. All models demonstrated structural integrity within safe limits.

Three-Dimensional Morphological Characterisation of Human Cortical Organoids Using a Customised Image Analysis Workflow

Summary Statement: A tailored image analysis workflow was applied to quantify cortical organoid health, development, morphology and cellular composition over time. The assessment of cellular composition and viability of stem cell-derived organoid models is a complex but essential approach to understanding the mechanisms of human development and disease. Aim: Our study was motivated by the need for an image-analysis workflow, including high-cell content, high-throughput methods, to measure the architectural features of developing organoids. We assessed stem cell-derived cortical organoids at 4 and 6 months post-induction using immunohistochemistry-labelled sections as the analysis testbed. The workflow leveraged fluorescence imaging tailored to classify cells as viable and dying or non-viable and assign neuronal and astrocytic perinuclear markers to count cells. Results/Outcomes: Image acquisition was accelerated by capturing the organoid slice in 3D using widefield-fluorescence microscopy. This method used computational clearing to resolve nuclear and perinuclear markers and retain their spatial information within the organoid’s heterogeneous structure. The customised workflow analysed over 1.5 million cells using DAPI-stained nuclei, filtering and quantifying viable and non-viable cells and the necrotic-core regions. Temporal analyses of neuronal cell number derived from perinuclear labelling were consistent with organoid maturation from 4 to 6 months of in vitro differentiation. Overall: We have provided a comprehensive and enhanced image analysis workflow for organoid structural evaluation, creating the ability to gather cellular-level statistics in control and disease models.

Dynamic Simulation of Photothermal Environment in Solar Greenhouse Based on COMSOL Multiple Physical Fields

Solar greenhouses are essential facilities for agricultural production in northern China, where uneven internal environments pose significant challenges. This study established a numerical model of photothermal conditions in solar greenhouses. Utilizing COMSOL MultiphysicsTM, we established a microclimate model that encompasses the greenhouse exterior and the soil directly below it, without considering the crops. This model coupled multiphysical fields with fluid flow and heat transfer processes. The boundary conditions and initial values of the external environment and soil were derived from meteorological data and an efficient interpolation function method, with the time step updated every 1h. The results demonstrate that the simulated values were in good agreement with the measured values. Our findings reveal the temporal dynamics of radiation and temperature changes, as well as spatial heterogeneity, within solar greenhouses under different winter weather conditions. Additionally, the potential of integrating with other real-time monitoring and control models was discussed. This study provides a theoretical foundation for developing microclimate models and predicting photothermal environments in greenhouses.

Identification of a circulating carbohydrate antigen as a highly specific and sensitive target for schistosomiasis serology.

The WHO 2030 roadmap deems diagnostics developments for schistosomiasis critically needed. Here we present identification of an antibody target with superior performance compared to traditionally used crude antigens in schistosomiasis serology. Access to unique controlled human infection model samples, traveler samples, and negative controls enabled this discovery, which forms the basis for development of new diagnostic tools urgently needed in travel medicine, surveillance in emerging transmission zones driven by climate change, and in pre- and post-elimination scenarios.

Expanded quasi-static models to predict the performance of robotic skins on soft cylinders

Robotic skins with embedded sensors and actuators are designed to wrap around soft, passive objects to control those objects from their surface. Prior state estimation and control models relied on specific actuator and sensor placement in robotic skins wrapped around soft cylinders, as well as used simplistic assumptions based on geometry and an ideal connection between the robotic skin and underlying structure. Such assumptions limit model fidelity and affect its utility in the design and control of surface-actuated systems. In this work, we relax prior assumptions and present a new quasi-static model with mechanics, controls, state estimation, and kinematic sub-models, or modules, for robotic skins placed around cylindrical structures. The kinematics module is used post-process to analyze the performance of the other three modules. We test the utility of the model on two robotic skin designs and compare the performance against a previous model and physical experiments. We demonstrate that the mechanics, controls, and state estimation modules presented herein outperform the previous model and the mechanics module can be used to predict the behavior of new robotic skin designs. The accuracy of the model increases as the stiffness of the host body material increases. This expanded theory could be utilized to reduce fabrication costs and speed up the design process and could be further extended to include system dynamics and model systems with multiple robotic skins.

Patient-derived cornea organoid model to study metabolomic characterization of rare disease: aniridia-associated keratopathy

BackgroundAniridia is a rare panocular disease caused by gene mutation in the PAX6, which is essential for eye development. Aniridia is inherited in an autosomal dominant manner, but its phenotype can vary significantly among individuals with the same mutation. Animal models, such as drosophila, zebrafish, and rodents, have been used to study aniridia through Pax6 deletions. Recently, patient-derived limbal epithelial stem cells (LESCs) and human-induced pluripotent stem cells (hiPSCs) have been used to model the disease in vitro, providing new insights into therapeutic strategies.MethodsIn this study, corneal organoids were generated from hiPSCs derived from aniridia patients with three different PAX6 nonsense mutations, allowing for a detailed comparison between diseased and healthy control models. These organoids structurally mimicked the human cornea and were used to investigate histologic and metabolomic differences between healthy and aniridia-derived samples.ResultsUntargeted metabolomic analysis revealed significant metabolic differences between wild-type (WT) and aniridia-associated keratopathy (AAK) hiPSCs. Further metabolomic profiling at different time points demonstrated distinct metabolic shifts, with amino acid metabolism pathways being consistently enriched in AAK organoids.ConclusionsThis study emphasizes the profound impact of AAK mutations on metabolism, particularly in amino acid biosynthesis and energy metabolism pathways.

Quadrotor Unmanned Aerial Vehicle-Mounted Launch Device Precision Analysis for Countering Intruding Drones

In light of the current issue of commercial and modified drones frequently intruding into sensitive locations such as airports, this paper proposes the use of quadrotor drones equipped with a launch device for interception. For an “X”-shaped quadrotor drone equipped with a two-degree-of-freedom gimbal launch device, dynamic, control, and ballistic models are constructed to analyze the impact of single-shot firing on the drone’s attitude and the factors affecting accuracy in rapid-fire scenarios. Simulation results indicate that downward firing has the highest shooting accuracy and is suitable for counter-drone missions. For single-shot firing, lateral downward firing compared to frontal firing can effectively reduce attitude and position changes, which is beneficial for engaging stationary targets. In the case of rapid fire, control of the firing interval is crucial for accuracy; a larger firing interval can significantly enhance shooting precision. When firing small payloads in rapid succession, vertical downward firing has the highest accuracy, while lateral firing results in a larger distribution radius of the impact points. To improve shooting accuracy, frontal firing is recommended. Future research will further explore the dynamic response of drones under different firing conditions and develop more advanced control strategies to enhance their practical performance and reliability.

Modeling and Optimization of Detention Basins For Cost Minimization and Flood Mitigation in Urban Areas

Objective: The objective of this study was to develop and apply an optimization model for the sizing of detention reservoirs in an urban watershed, aiming to minimize implementation costs and mitigate flood risks in critical areas. Theoretical Framework: The study is based on flood control models and urban drainage infrastructure, focusing on optimization techniques applied to water resource management. References include the SCS Curve Number method for surface runoff, the PCSWMM hydrological model for precipitation simulations, and the HEC-RAS hydraulic model for flood area delineation. Method: The research was divided into three main stages: (i) physical and hydrological characterization of the watershed using PCSWMM; (ii) delineation of flood-prone areas with HEC-RAS 2D; and (iii) development of a nonlinear optimization model in GAMS for the sizing of detention reservoirs. The decision variables included the height, surface area, and outlet diameter of the reservoirs, with implementation cost as the main objective function. Results and Discussion: The optimization modeling determined the ideal characteristics of six reservoirs distributed throughout the watershed to maximize retention and flow control. The total flooded area was estimated at 270,989 m², with maximum depths reaching 1.914 meters. The total cost of the reservoir system was calculated at approximately R$ 1,164,845.91, providing an optimized solution for flood control in the watershed’s vulnerable areas. Research Implications: The results suggest a practical and feasible solution for flood control in urban watersheds, offering a replicable model that can aid in planning drainage infrastructure in other regions facing similar challenges. Originality/Value: This study is original in its integrated application of PCSWMM and HEC-RAS models with a nonlinear optimization approach in GAMS, resulting in an efficient and economical solution for flood mitigation in urbanized areas.

Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework

This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. The goal is to provide accurate early warning systems and enhanced risk management tools for power companies. The BP neural network algorithm adjusts weights to minimize prediction errors, while multi-scale feature learning captures the diversity and regularity of user behavior by extracting data from various time dimensions, such as daily, weekly, and monthly intervals. First, electricity usage and weather data from the UMass Smart Dataset are preprocessed, including steps such as data cleaning, standardization, and normalization. Next, features are extracted across three time scales—daily, weekly, and monthly. These features are then input into the BP neural network model using the multi-scale feature learning method. A hierarchical neural network structure is designed to address the characteristics of different scales in distinct layers. Key model parameters are optimized, and a sensitivity analysis is conducted. The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. Specifically, the Gini coefficient is 0.55, the Kolmogorov-Smirnov statistic is 0.60, the Matthews correlation coefficient is 0.45, and specificity is 0.82. These results indicate that the proposed method offers significant improvements in capturing user behavior patterns and enhancing prediction accuracy. The study concludes that the effective fusion of multi-scale features not only enhances the model’s prediction performance but also strengthens its generalization ability. This method provides an advanced risk management tool for power companies, helping to increase the operational efficiency of smart grids and encouraging further research toward greater intelligence in the field.

Challenges and Countermeasures in Cross-Border Data Collection

In the context of globalization, cross-border data collection has become a crucial tool for countries combating transnational crime. China primarily employs international criminal judicial assistance to conduct cross-border data collection. While this approach respects national sovereignty, it faces practical challenges such as low efficiency and limited authority. In contrast, the United States and the European Union adopt a more flexible data controller model, achieving robust cross-border data collection capabilities. However, conflicts in data sovereignty and differences in legal systems hinder the direct application of these models in China. This paper employs a comparative research methodology to analyze the legislative frameworks and backgrounds of the U.S. and EU cross-border data collection approaches, identifying reasons these models are unsuitable for direct implementation in China. Building on insights from these legislative models, the paper proposes recommendations such as increasing the number of bilateral judicial assistance agreements and establishing a categorized data management system, aiming to provide a reference for China's future development in this field.

Application of Ultrashort Implants in Posterior Maxilla With Insufficient Bone Height: A Finite Element Analysis.

Implantation of the posterior maxilla with insufficient bone height faces challenges. Studies have shown that the use of ultrashort implants can avoid additional damage. This finite element analysis study aimed to evaluate the impacts of different lengths of ultrashort implants and three surgical approaches on stress, strain, and displacement in the posterior maxilla with varying bone heights. Twelve models of different lengths (3.0, 4.0, 5.0, and 6.0 mm) of ultrashort implants combined with unicortical fixation, bicortical fixation, and transalveolar sinus elevation were established, and conventional implants and short implants were considered as control models. Von Mises stresses within the implants and the sinus floor cortical bone, maximum and minimum principal stresses within the alveolar ridge cortical bone, and the maximum principal strain of the cancellous bone were determined using these models. Additionally, the displacement of the implants was analysed. Stress distribution range and peak values increased as implant length decreased. In the ultrashort implant group, H5L6 exhibited the smallest maximum and minimum principal stresses, 35.77 and 10.66 MPa, respectively. Among groups with different bone heights, 6 mm long implants presented the lowest maximum von Mises stress, whereas 3 mm long implants presented the highest. In the posterior maxilla with bone heights of 3, 4, and 5 mm, the stresses in different lengths of ultrashort implants and surrounding bone tissue were lower than the yield strengths in these areas, and the use of 6 mm-long ultrashort implants combined with osteotome sinus floor elevation can achieve lower stress distribution. This study provides important insights into the biomechanical properties of ultrashort implants combined with three different surgical procedures in severely atrophic maxilla. The 6-mm long implant combined with osteotome sinus floor elevation is most suitable for this area.